Competitors.
blackOur conference version [1]} uses canny edges and lines is denoted as ZITS [1]}, while ZITS++ indicates our improved version in this paper enhanced by the priors of CATS, gradients and lines, as well as several novel modules and techniques. 
We compare the proposed model with other state-of-the-art methods, which include Edge Connect (EC) [3]}, Contextual Residual Aggregation (HiFill) [4]}, Multi-scale Sketch Tensor inpainting (MST) [5]}, Co-Modulation GAN (Co-Mod) [6]}, blackMask-Aware Transformer (MAT) [7]}, and Large Mask inpainting (LaMa) [8]}. All competitors are compared in the Places2. We also retrain EC, MST, and LaMa for the Indoor dataset to discuss the structure recovery. Note that the LaMa compared below are all trained with the same total steps as ZITS++.
